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DOC-800 | New GraphRAG UI steps #805

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new graphrag ui
nerpaula Oct 13, 2025
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Merge branch 'main' into DOC-800
nerpaula Oct 22, 2025
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finalize content, remove unnecessary screenshots, apply to all versions
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127 changes: 78 additions & 49 deletions site/content/3.12/data-science/graphrag/web-interface.md
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Original file line number Diff line number Diff line change
@@ -1,15 +1,14 @@
---
title: How to use GraphRAG in the ArangoDB Platform web interface
title: How to use GraphRAG in the Arango Data Platform web interface
menuTitle: Web Interface
weight: 5
description: >-
Learn how to create, configure, and run a full GraphRAG workflow in four steps
using the Platform web interface
Learn how to create, configure, and run a full GraphRAG workflow in just a few steps
---
{{< tag "ArangoDB Platform" >}}
{{< tag "AI Services" >}}

{{< tip >}}
The ArangoDB Platform & GenAI Suite is available as a pre-release. To get
The Arango Data Platform & AI Services is available as a pre-release. To get
exclusive early access, [get in touch](https://arangodb.com/contact/) with
the ArangoDB team.
{{< /tip >}}
Expand All @@ -19,28 +18,39 @@ the ArangoDB team.
The entire process is organized into sequential steps within a **Project**:

1. Creating the importer service
2. Uploading your file and exploring the generated Knowledge Graph
3. Creating the retriever service
4. Chatting with your Knowledge Graph
2. Adding data sources
3. Exploring the generated Knowledge Graph
4. Creating the retriever service
5. Chatting with your Knowledge Graph

## Create a GraphRAG project

To create a new GraphRAG project using the ArangoDB Platform web interface, follow these steps:
To create a new GraphRAG project using the Arango Data Platform web interface, follow these steps:

1. From the left-hand sidebar, select the database where you want to create the project.
2. In the left-hand sidebar, click **GenAI Suite** to open the GraphRAG project management
2. In the left-hand sidebar, click **AI Services** to open the GraphRAG project management
interface, then click **Run GraphRAG**.
3. In the **GraphRAG projects** view, click **Add new project**.
4. The **Create GraphRAG project** modal opens. Enter a **Name** and optionally
a description for your project.
5. Click the **Create project** button to finalize the creation.

## Project Settings

The **Project Settings** dialog allows you to configure and manage your
Importer and Retriever services.

You can open the **Project Settings** dialog in two ways:
- In the **Data Sources** section, click **Add data source** and then click on
the **Open project settings** button.
- In the **Graph** section, click on the gear icon.

## Configure the Importer service

Configure a service to import, parse, and retrieve all the needed data from a
Configure a service to import, parse, and extract all the needed data from a
file. This service uses the LLM API provider and model of your choice.

After clicking on a project name, you are taken to a screen where you can
After opening the **Project Settings**, you are taken to a dialog where you can
configure and start a new importer service job. Follow the steps below.

{{< tabs "importer-service" >}}
Expand All @@ -52,23 +62,21 @@ configure and start a new importer service job. Follow the steps below.
3. Enter your **OpenAI API Key**.
4. Click the **Start importer service** button.

![Configure Importer service using OpenAI](../../../images/graphrag-ui-configure-importer-openai.png)
{{< /tab >}}

{{< tab "OpenRouter" >}}
1. Select **OpenRouter** from the **LLM API Provider** dropdown menu.
2. Select the model you want to use from the **Model** dropdown menu. By default,
the service is using **Mistral AI - Mistral Nemo**.
1. Enter your **OpenAI API Key**.
2. Enter your **OpenRouter API Key**.
3. Click the **Start importer service** button.
the service uses **Mistral AI - Mistral Nemo**.
3. Enter your **OpenAI API Key**.
4. Enter your **OpenRouter API Key**.
5. Click the **Start importer service** button.

{{< info >}}
When using the OpenRouter option, the LLM responses are served via OpenRouter
while OpenAI is used for the embedding model.
When using OpenRouter, you need both API keys because the LLM responses are served
via OpenRouter while OpenAI is used for the embedding model.
{{< /info >}}

![Configure Importer service using OpenRouter](../../../images/graphrag-ui-configure-importer-openrouter.png)
{{< /tab >}}

{{< tab "Triton LLM Host" >}}
Expand All @@ -81,38 +89,59 @@ Note that you must first register your model in MLflow. The [Triton LLM Host](se
service automatically downloads and loads models from the MLflow registry.
{{< /info >}}

![Configure Importer service using Triton](../../../images/graphrag-ui-configure-importer-triton.png)
{{< /tab >}}

{{< /tabs >}}

See also the [GraphRAG Importer](services/importer.md) service documentation.
See also the [Importer](services/importer.md) service documentation.

## Upload your file
## Add data source

1. Upload a file by dragging and dropping it in the designated upload area.
The importer service you previously launched parses and creates the
Knowledge Graph automatically.
2. Enter a file name.
3. Click the **Start import** button.
To add your first data source:

1. In the **Data Sources** section, click the **Add data source** button.
2. Upload a file by dragging and dropping it in the designated upload area.
The importer service you previously configured will automatically parse the file
and create the Knowledge Graph.
3. Enter a descriptive name for your file.
4. Click the **Start import** button.

{{< info >}}
You can only import a single file, either in `.md` or `.txt` format.
Currently, you can import one file at a time in either Markdown (`.md`) or
plain text (`.txt`) format. Additional files can be added to update the Knowledge Graph.
{{< /info >}}

![Upload file in GraphRAG web interface](../../../images/graphrag-ui-upload-file.png)

## Explore the Knowledge Graph

You can open and explore the Knowledge Graph that has been generated by clicking
on the **Explore in visualizer** button.
After your file is processed, you can view and explore the generated Knowledge Graph
in the **Graph** section.

![Explore Knowledge Graph in GraphRAG web interface](../../../images/graphrag-ui-explore-knowledge-graph.png)

For a more detailed exploration, click the **Explore** button to open the Knowledge Graph in the dedicated Graph Visualizer.

For more information, see the [Graph Visualizer](../../graphs/graph-visualizer.md) documentation.

## Update the Knowledge Graph

Once you have created your initial Knowledge Graph, you can update it by uploading
additional files using the same process described in the [Add data source](#add-data-source) section.
The importer service will automatically update the existing Knowledge Graph and
underlying collections with the new data.

To update your Knowledge Graph:

1. In the **Data Sources** section, click the **Add data source** button again.
2. Upload a new file by dragging and dropping it in the designated upload area.
3. The importer service will process the new file and update the existing Knowledge Graph along with the underlying collections.

## Configure the Retriever service

Creating the retriever service allows you to extract information from
the generated Knowledge Graph. Follow the steps below to configure the service.
The retriever service enables you to query and extract information from
the generated Knowledge Graph. To configure the retriever service, open the
**Project Settings** and follow the steps below.

{{< tabs "retriever-service" >}}

Expand All @@ -123,7 +152,6 @@ the generated Knowledge Graph. Follow the steps below to configure the service.
3. Enter your **OpenAI API Key**.
4. Click the **Start retriever service** button.

![Configure Retriever Service using OpenAI](../../../images/graphrag-ui-configure-retriever-openai.png)
{{< /tab >}}

{{< tab "OpenRouter" >}}
Expand All @@ -134,11 +162,10 @@ the generated Knowledge Graph. Follow the steps below to configure the service.
4. Click the **Start retriever service** button.

{{< info >}}
When using the OpenRouter option, the LLM responses are served via OpenRouter
while OpenAI is used for the embedding model.
When using OpenRouter, the LLM responses are served via OpenRouter while OpenAI
is used for the embedding model.
{{< /info >}}

![Configure Retriever Service using OpenRouter](../../../images/graphrag-ui-configure-retriever-openrouter.png)
{{< /tab >}}

{{< tab "Triton LLM Host" >}}
Expand All @@ -151,26 +178,28 @@ Note that you must first register your model in MLflow. The [Triton LLM Host](se
service automatically downloads and loads models from the MLflow registry.
{{< /info >}}

![Configure Retriever Service using Triton](../../../images/graphrag-ui-configure-retriever-triton.png)
{{< /tab >}}

{{< /tabs >}}

See also the [GraphRAG Retriever](services/retriever.md) documentation.
See also the [Retriever](services/retriever.md) documentation.

## Chat with your Knowledge Graph

The Retriever service provides two search methods:
- [Local search](services/retriever.md#local-search): Local queries let you
explore specific nodes and their direct connections.
- [Global search](services/retriever.md#global-search): Global queries uncover
broader patters and relationships across the entire Knowledge Graph.

![Chat with your Knowledge Graph](../../../images/graphrag-ui-chat.png)
The chat interface provides two search methods:
- **Instant search**: Instant queries provide fast responses.
- **Deep search**: This option will take longer to return a response.

In addition to querying the Knowledge Graph, the chat service allows you to do the following:
- Switch the search method from **Local Query** to **Global Query** and vice-versa
- Switch the search method from **Instant search** to **Deep search** and vice-versa
directly in the chat
- Change the retriever service
- Change or create a new retriever service
- Clear the chat
- Integrate the Knowledge Graph chat service into your own applications

## Integrate the Knowledge Graph chat service into your application

To integrate any service into your own applications,
go to **Project Settings** and use the copy button next to each service to
copy its integration endpoint. You cam make `POST` requests to the endpoints
with your queries, the services accept `JSON` payloads and return structured
responses for building custom interfaces.
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